The present invention relates to a method and a system for diagnosing a thermal power plant system.
In process control of a thermal power plant, automatic power plant control systems (hereinafter referred to as APC systems) of an analog type which perform conventional PID control using feedback signals of the process state variables have been used. An example of APC system is shown in FIG. 1, which comprises a PID arithmetic operation unit 21 in the form of control algorithm performing proportional (P) action plus integral (I) action plus differential (D) action. State variables FB are fed back from a thermal power plant 1, and deviation DEV of each of the state variables FB from the corresponding target value or set value SV is determined by an adder 22. The deviation DEV is fed to the PID arithmetic operation unit 21, which produces a manipulated variable ACT.
In FIG. 1, only a single control loop is illustrated, but APC systems for a large-sized thermal power plant of today have several tenths of control loops and they frequently interfere with each other.
As an example, a general arrangement of a thermal power plant and an APC system therefor are shown in FIGS. 2A and 2B. As shown in FIG. 2A, fuel is injected by means of a fuel injection pump FIP into a burner of a boiler, while air is supplied by means of an air fan into the burner so that combustion occurs. Water is fed by means of a boiler feed pump BFP into the boiler and made to rise along the walls of the boiler to become steam and superheated by a superheater SH. Water is sprayed by a spray device SP on the steam to control the temperature of the superheated steam. The superheated steam is passed through a high pressure turbine HP. The steam that has been passed through the high pressure turbine HP is returned to the boiler and is reheated by a reheater RH and is then passed through middle and low pressure turbines LP to drive them. The turbines HP and LP are connected to an electric generator G. The steam is thereafter condensed in a condenser COND to become water, which is then fed to the boiler feed pump BFP. A gas damper GD is used to adjust the flow rate of a high temperature gas through the reheater RH and thereby control the temperature of the reheated steam.
The fuel injection pump FIP operates in compliance with a fuel injection rate command value FIR.sub.c given as a manipulated variable from the APC system (FIG. 2B), while the actual fuel injection rate FIR.sub.f is detected and fed back as a state variable to the APC system. Similarly, the air injection rate is adjusted by means not shown in compliance with an air injection rate command value AIR.sub.c given as a manipulated variable from the APC system, while the actual air injection rate AIR.sub.f is detected and fed back as a state variable to the APC system. Similarly, the feed water flow rate is adjusted by means of the boiler feed pump BFP in compliance with a feed water flow rate command value FWF.sub.c given as a manipulated variable from the APC system, while the actual feed water fuel flow rate FWF.sub.f is detected and fed back as a state variable to the APC system. The flow rate of the sprayed water is adjusted by means of a spray valve in compliance with a superheater spray command value SP given as a manipulated variable from the APC system, while the main steam temperature MST is detected and fed back as a state variable to the APC system. The position or opening of the gas damper GD is adjusted in compliance with a gas damper position command value GD given as a manipulated variable from the APC system, while the reheater steam temperature is detected and fed back as a state variable to the APC system. In addition to the manipulated variables FWF.sub.c, SP, FIR.sub.c, AIR.sub.c and GD which themselves are applied to final control elements, there is a manipulated variable, a firing rate command value FR, which acts to determine other manipulated variable(s). The blocks 23-28 provided at the lines (conductors) carrying the manipulated variables are stations which permit selection between automatic control and manual control. The blocks with a mark ".SIGMA." are adders. The blocks with a mark "K" are proportional elements. The blocks with a mark "D" are differential elements. The blocks with a mark ".intg." are integral elements. The blocks with a mark "K+.intg." are proportional plus integral elements. The blocks with a mark "f" are function generators. MWD denotes a load demand. MST.sub.s denotes a main steam temperature set value. RHT.sub.s denotes a reheater steam temperature set value.
It will be seen that each of the manipulated variables is determined not necessarily from one state variable but the loops for determining the manipulated variables are interrelated.
Although not shown in FIG. 2B, a main steam pressure MSP is also detected and fed back as a state variable to the APC system and used for the control.
Control by the APC system alone has however the following limitations.
(1) While the characteristics of an electric power plant have non-linearity having the load as a parameter, the parameters of the PID actions determining the control capability usually have only a single pattern.
(2) A main disturbance to a thermal power plant in its normal operation is variation in the load. The heat capacity of a boiler varies depending on the load. In an attempt to compensate for the variation of the heat capacity, a boiler input accelerating signal BIR (FIG. 2B) is added to increase the fuel in advance of the predicted temperature drop. But this alone is not sufficient.
(3) In recent years, there is an increasing demand on a thermal power plant for improvement in load response capability, particularly improvement in the load variation rate, but realization of such improvement is obstructed by limitation in the control capability of some of the plant state variables, particularly the main steam temperature and the reheater steam temperature.
In an attempt to solve these problems, a direct digital control system (hereinafter referred to as DDC system) is used to realize optimum control over an electric power plant, using a control model formed of a mathematical model representing or expressing the thermal power plant as being controlled by the APC system and hence to realize complementary control. By such complementary control, the control capability of the main steam temperature and the reheater steam temperature have been substantially improved. FIG. 3 shows a system where an autoregressive model (hereinafter referred to as AR model) is used as the mathematical model.
But before further describing the optimum control system shown in FIG. 3, description on an AR model used as a mathematical control model is given.
An AR model with respect to a single variable is expressed by the following equation (1). ##EQU1## where
s represents a sampling instance, it being assumed that sampling of the plant variables is conducted at a regular interval,
x(s) represents the state variable at the sampling instance s,
a(m) represents an AR model coefficient,
M represents the order of the AR model, and
u(s) represents a white noise at the sampling instance s.
A series of the values of u(s) for successive sampling instances s constitute a white noise series.
If one expands the above equation of the AR model for a single variable, one obtains an equation (2) of the AR model for multiple variables, viz., ##EQU2## where
s represents a sampling instance,
X(s) represents a multiple state variable vector,
A(m) represents an AR model coefficient matrix,
M represents the order of the AR model, and
U(s) represents a white noise vector
When the combination of the thermal power plant and the APC system controlling the thermal power plant is to be expressed by a mathematical model, the expanded AR model (expressed by the equation (2)) can be used. Determination of the coefficient matrix is called "identification". It essentially consists of two steps, namely (1) an identification test for collecting dynamics data of the thermal power plant under control of the APC system, and (2) modelling for determining the AR model coefficient matrix.
The data collection for the identification test is conducted by applying, to the respective manipulation terminals of the plant 1 being controlled by the APC system 2 as shown in FIG. 4, white noises WN independent of each other, e.g., M series (maximum length linear shift register sequences), from a white noise generating unit 90, and by collecting time series of the manipulation variables MV and the state variables FB of the plant by a data collecting unit 91. Thus the dynamics data concerning the controlled object 5 for the DDC system, i.e., the thermal power plant being controlled by the APC system are collected.
The determination of the multiple variable AR model coefficient matrix A(m) for the modelling, together with determination of the order M of the AR model is conducted in accordance with the dynamics data of the controlled object 5 collected by the identification test and by means, e.g., of TIMSAC (time series analysis and controller design program) library.
When an AR model is used for DDC, it is better in the modelling to divide the variables into the state variables of the plant (feedback information of pressure, temperature, flow rate and the like) and the manipulated variable to be applied to the plant (control information of firing rate command value, spray valve position command and the like) because such division makes easier the arithmetic operation to be conducted at each sampling instance. Therefore, the AR model is expressed by the following expression (3) using state vector Z and the manipulation vector Y. EQU Z.sub.i (s+1)=B(i)Z.sub.0 (s)+Z.sub.i+1 (s)+C(1)Y(s) (3)
where
i=0, 1, 2, . . . , M-1, M being the order of the AR model,
B represents the AR model coefficient matrix for the state vector Z,
C represents the AR model coefficient matrix for the manipulation vector Y,
Z.sub.0 (s+1) represents predicted values of the state vector Z.sub.0 (s+1) for the sampling instance (s+1), i.e., the sampling instance immediately subsequent to the sampling instance s.
When the above expression of the equation (3) is used, the identification comprises determination of the order M of the AR model, and determination of the AR model coefficient matrixes B and C. Thus the combination of the thermal power plant 1 and the APC system 2 controlling the thermal power plant 1 is expressed by a control AR model 4.
Then optimum manipulation vector G which minimizes an evaluation function J defined by the following equation (4): ##EQU3## where
K represents the evaluation time,
X.sup.T (i) represents a transposed matrix of X(i),
Q represents a weighting factor matrix of the state vector,
Y.sup.T (i-1) represents a transposed matrix of Y(i-1), and
R represents a weighting factor matrix of the manipulation vector, is determined in advance using the control AR model 4 expressed by the equation (3). At each sampling instance during on-line control, a value given by EQU Y(s+1)=G.multidot.Z(s+1)
is added to the APC system output to determine the value of the manipulated variable.
A single control loop for such optimum control is shown in FIG. 5.
The DDC system 3 supplies a time width output of either INC (increase) or DEC (decrease) responsive to the value (supplementary manipulated value) determined by the DDC system 3 to an analog memory (AM) 230 whose output DCT, which is a voltage signal having a magnitude and a sign indicative of the supplementary value, is added to the output of the APC system 2 at the adder 231, and the sum is used as a set value to be applied to a setting device 232, which in turn produces a manipulated variable MV. In such an optimum control system, the output ACT of the APC system 2 is determined as a result of the PID arithmetic operation and is normally not of an excessive value, while the output DCT of the DDC system 3 may become excessive for the plant.
It is therefore necessary to provide a supervising system to conduct supervision of the plant. An example of supervising system is shown in FIG. 6. In this system, (1) differences between predicted values Z(s+1) of the state variables at the next (immediately subsequent) sampling instance predicted by the control AR model 4 based on the actual plant variables at the sampling instance s and the actual values Z(s+1) are, upon demand from the operator, displayed on a display device 310, in the form of trend output, and (2) each of the manipulated variables MV as applied to the plant is compared with a variable limit value which varies depending on the load command in a manipulated variable supervising unit 311, which judges that the manipulated variable MV is abnormal when it exceeds the limit value, and causes the display device 310 to produce an alarm output and cause the manipulated variable to resume the value at the immediately preceding sampling instance.
But the determination of the limit values, which must of course be done taking into consideration the characteristics of the plant, is a difficult task. It has been done relying on experience of operation and it has been difficult to make sure that the set values are appropriate. It has therefore been difficult to conduct objective or consistent diagnosis on the manipulated variables. Moreover, although use of the control AR model 4 enables prediction of the state variables FB, it was not possible to make prediction of the manipulated variables MV.
In another conventional system, a diagnostic model in the form of an AR model representing the combination of the plant and the APC system constituting the diagnosed object and expressed by: ##EQU4## is used to produce, based on the actual state variables at the sampling instance s, predicted values Z(s+1) for the next sampling instance (s+1). At the next sampling instance (s+1), the differences between the predicted values Z(s+1) and the actual state variables Z(s+1) are determined by: EQU E(s+1)=Z(s+1)-Z(s+1) (6),
and the whiteness level of time series of the differences E(s+1) is tested according to the auto-correlation function of the series and occurrence or nonoccurrence of abnormality is judged by such test. In this way, diagnosis of the plant is conducted. But such diagnosis is not satisfactory for a thermal power system, which is a dynamic system where a large number of variables are interrelated and a single fault may cause abnormalities in two or more variables. No diagnosing system has so far been realized which is capable of providing information on the cause of abnormality and permitting continuous supervision of the change of the degree of abnormality in real time (on on-line basis) and dynamically, i.e., as a function of time. Neither has any diagnosing system for the plant system with the APC system as well as the DDC system been realized.